

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			@slow
			@require_torch
			def   UpperCamelCase		(     self							)	->    Dict:
							'''simple docstring'''






							A__          =      EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"   ,   "prajjwal1/bert-tiny"							)
							A__          =      BertTokenizer.from_pretrained("bert-base-uncased"							)

							A__          =      bertabert.config.encoder.vocab_size
							A__          =      tokenizer.sep_token_id
							A__          =      tokenizer.cls_token_id
							A__          =      128

							A__          =      datasets.load_dataset("cnn_dailymail"   ,   "3.0.0"   ,   split="train[:1%]"							)
							A__          =      datasets.load_dataset("cnn_dailymail"   ,   "3.0.0"   ,   split="validation[:1%]"							)

							A__          =      train_dataset.select(range(32							)							)
							A__          =      val_dataset.select(range(16							)							)

							A__          =      4

							def _map_to_encoder_decoder_inputs(lowercase							):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											A__          =      tokenizer(batch["article"]   ,   padding="max_length"   ,   truncation=UpperCAmelCase_   ,   max_length=512							)
											A__          =      tokenizer(batch["highlights"]   ,   padding="max_length"   ,   truncation=UpperCAmelCase_   ,   max_length=128							)
											A__          =      inputs.input_ids
											A__          =      inputs.attention_mask

											A__          =      outputs.input_ids
											A__          =      outputs.input_ids.copy()
											A__          =      [
											    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
											]
											A__          =      outputs.attention_mask

											assert all(len(UpperCAmelCase_							) == 512 for x in inputs.input_ids							)
											assert all(len(UpperCAmelCase_							) == 128 for x in outputs.input_ids							)

											return batch

							def _compute_metrics(lowercase							):
											A__          =      pred.label_ids
											A__          =      pred.predictions

											# all unnecessary tokens are removed
											A__          =      tokenizer.batch_decode(UpperCAmelCase_   ,   skip_special_tokens=UpperCAmelCase_							)
											A__          =      tokenizer.batch_decode(UpperCAmelCase_   ,   skip_special_tokens=UpperCAmelCase_							)

											A__          =      sum([int(pred_str[i] == label_str[i]							) for i in range(len(UpperCAmelCase_							)							)]							) / len(UpperCAmelCase_							)

											return {"accuracy": accuracy}

							# map train dataset
							A__          =      train_dataset.map(
							    _map_to_encoder_decoder_inputs   ,   batched=UpperCAmelCase_   ,   batch_size=UpperCAmelCase_   ,   remove_columns=["article", "highlights"]   ,   )
							train_dataset.set_format(
							    type="torch"   ,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]   ,   )

							# same for validation dataset
							A__          =      val_dataset.map(
							    _map_to_encoder_decoder_inputs   ,   batched=UpperCAmelCase_   ,   batch_size=UpperCAmelCase_   ,   remove_columns=["article", "highlights"]   ,   )
							val_dataset.set_format(
							    type="torch"   ,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]   ,   )

							A__          =      self.get_auto_remove_tmp_dir()

							A__          =      SeqaSeqTrainingArguments(
							    output_dir=UpperCAmelCase_   ,   per_device_train_batch_size=UpperCAmelCase_   ,   per_device_eval_batch_size=UpperCAmelCase_   ,   predict_with_generate=UpperCAmelCase_   ,   evaluation_strategy="steps"   ,   do_train=UpperCAmelCase_   ,   do_eval=UpperCAmelCase_   ,   warmup_steps=0   ,   eval_steps=2   ,   logging_steps=2   ,   )

							# instantiate trainer
							A__          =      SeqaSeqTrainer(
							    model=UpperCAmelCase_   ,   args=UpperCAmelCase_   ,   compute_metrics=_compute_metrics   ,   train_dataset=UpperCAmelCase_   ,   eval_dataset=UpperCAmelCase_   ,   tokenizer=UpperCAmelCase_   ,   )

							# start training
							trainer.train()



"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





